Chronic lymphocytic leukemia (CLL) is a disease of nonproliferating and mature-appearing B lymphocytes. Insulin-like growth factor-1 (IGF-1) is a small peptide hormone and has mitogenic and antiapoptotic effects, and insulin-like growth factor binding protein-3 (IGFBP-3) has antiproliferative effects on cells. In this study, we investigated plasma levels of both IGF-1 and IGFBP-3 in patients with CLL compared with controls, and we compared these plasma levels according to prognostic factors.Patients with newly diagnosed CLL who were being followed at the Haseki Training and Research Hospital, İstanbul, Turkey, and volunteers were included in this study. Patients were stratified according to the Rai staging system. Statistical analysis was conducted using SPSS 17.0 for Windows.Forty-three patients [16 women (37%) and 27 men (63%)] were enrolled in this study. Twenty-one volunteers  were included in the control group. The median age of the patients was 65±9 years (range: 18-63 years), and subjects in the control group were 68±8 years old (range: 18-63 years). Even though the plasma levels of IGF-1 were higher and those of IGFBP-3 were lower and the ratio of IGF-I/IGFBP-3 was higher in comparison with the control group, these differences were not statistically significant (p>0.05). In the study group, IGF-1 levels appeared to be increased in parallel to more advanced Rai stages. There were no significant differences between the other groups (p=0.105).Plasma IGF-I levels were found higher in patients than in the control group and plasma IGFBP-3 levels were lower; however, neither result was statistically significant. Plasma IGF level increment was observed in concordance with Rai staging. These results prompted us to think that plasma IGF-1 levels in CLL patients are correlated with tumor burden and Rai staging and therefore could be a valuable prognostic factor. Further comprehensive studies are required to support our results. Chronic lymphocytic leukemia (CLL) is a disease of nonproliferating and mature-appearing B lymphocytes. Most patients with CLL are elderly; just 10% are aged less than 50 years. In the evaluation of the prognosis of patients with CLL, mutations and cytogenetic abnormalities are crucial and independent markers in addition to clinical classifications .Insulin-like growth factor (IGF) has a pivotal role in the normal development of fetuses and children. In adulthood, this growth factor has a role in the inhibition of cell proliferation and apoptosis, in addition to its role in cellular metabolism. IGF-1 is a small peptide hormone that has mitogenic and antiapoptotic effects, but IGF-binding protein 3 (IGFBP-3) has an antiproliferative effect and negates the mitogenic effects of IGF-1 by stimulating apoptosis. In several types of tumors, it has been shown that IGF-1 levels are increased and IGFBP-3 levels are decreased .In this study, we investigated plasma levels of both IGF-1 and IGFBP-3 in patients with CLL compared with controls and we compared these plasma levels according to prognostic factors.Patients who were newly diagnosed with CLL at the Haseki Training and Research Hospital and healthy volunteers were included in this study. Patients with the following conditions were excluded from the study: chronic renal disease, decompensated heart failure, chronic hepatitis, coronary artery disease, diabetes mellitus with organ damage or uncontrolled plasma glucose levels, chronic inflammatory disease, and major trauma in the last the year. Twenty-one volunteers were included in the control group.Plasma samples were obtained after centrifugation at 3500 rpm for 8 min and stored at -80 °C until analysis. Plasma IGF-I detection was performed using an ELISA kit  in accordance with the manufacturer’s protocol. The sensitivity of the kit was 0.15 ng/mL. IGFBP-3 levels were measured using a BioSource ELISA Kit with solid-phase enzyme immunoassay . The analytical sensitivity of the kit was 10 μg/mL. Samples were measured using an ELISA microplate reader .Statistical analysis was conducted using SPSS 17.0 for Windows . The Kolmogorov-Smirnov test was used to determine whether the samples were from a population with normal distribution. In the comparison of values between two groups, Student’s t-test was used if the group was distributed normally. If the group was not normally distributed, the Mann-Whitney U test was used. In the analysis of proportional data, the chi-square test was used. Pearson and Spearman correlation tests were used to compare numerical parameters. One-way ANOVA testing was used to compare more than two groups; post hoc Bonferroni testing was used for multiple comparisons. In all statistical assessments, the cut-off level of statistical significance was assumed as p<0.05.Study assessment and methods were approved by the local institutional ethics committee. Patient demographics and laboratory data were obtained from patient records upon obtaining oral informed consent from the patients and their information was recorded.Forty-three patients [16 women (37%) and 27 men (63%)] were enrolled in the study. Twenty-one control subjects who were demographically compatible  were included in the control group. The median age of the patient and control groups was 65±9 years (range: 18-63 years) and 68±8 years (range: 18-63 years), respectively. There was no statistical demographic difference between the two groups (p>0.05).Among the patients, 44% (n=19) had CLL of Rai stage 0, 11% (n=5) stage I, 20% (n=9) stage II, 16% (n=7) stage III, and 6% (n=3) stage IV.Compared with the control group, IGF-1 levels were found to be higher in the study group (531±246 ng/mL), and IGF-1 levels were also detected to be subsequently higher in every Rai stage . However, this difference was not statistically significant (p>0.05). IGFBP-3 levels were found lower in the study group (3890±324 ng/mL) and IGFBP-3 levels were also found lower in each sequential Rai stage , but this was not statistically significant (p>0.05). The IGF-I/IGFBP-3 ratio was higher in the study group (0.32±0.60 ng/mL), although the difference was not statistically significant (p=0.5) .In patients with CLL, we observed that IGF-1 levels had a positive correlation with Rai stages . The same correlation was not observed for IGFBP-3 levels . IGF-1 levels in the patients with CLL appeared to increase in parallel with more advanced Rai stages. There were no significant differences among Rai stages (p=0.105). When IGFBP-3 levels were compared according to stage, there were also no statistical significant differences (p=0.3)  and 2.The IGF system plays a pivotal role in normal growth throughout fetal and childhood development. In adult life, this system continues to function by regulating normal cellular metabolism, proliferation, and differentiation and it protects against apoptotic signals. However, aberrant stimulation can contribute to the development and progression of malignant growth ,5,6.It has been shown in cell cultures that IGFBP-3 inhibits DNA synthesis without IGF. It has been claimed that IGFBP-3 may link p53 to potential novel autocrine/paracrine signaling pathways and to processes regulated by or dependent on IGF(s), such as cellular growth, transformation, and survival. It has also been asserted that induction of IGFBP-3 gene expression by wild-type, but not mutant, p53 was associated with enhanced secretion of an active form of IGFBP-3 capable of inhibiting mitogenic signaling by IGF-1 .Many studies have reported that high levels of IGF-I, low levels of IGFBP-3, or increments of the molar ratio of IGF-I/IGFBP-3 were associated with various types of cancers ,9,10,11.In our study, the mean plasma IGF-I levels of the patients were found higher than those of the control group, but the difference was not statistically significant. Plasma IGFBP-3 level was lower than in the control group, but this was also not statistically significant. However, plasma IGF level increments were in parallel with Rai stages. A reverse correlation was not observed for IGFBP-3 levels.Molica et al. measured serum levels of IGF-1 and IGFBP-3 in 77 patients with CLL and found them to be statistically significantly lower than in the control group. However, no significant correlation was found between serum levels of either IGF-1 or IGFBP-3 and clinicohematologic variables including age, sex, Rai clinical stages, serum levels of lactate dehydrogenase and beta-2 microglobulin, peripheral blood lymphocyte count, and lymphocyte doubling time .In our study IGF-1 and IGFBP-3 levels were lower in patients with Rai stage 0 compared with the control group. Although IGF-1 levels were found lower in early stages, they increased significantly in parallel with more advanced Rai stages.In conclusion, plasma IGF-I levels in CLL patients were found higher than in the control group and plasma IGFBP-3 levels were lower. However, neither result was statistically significant. The increments of plasma IGF-1 level were in parallel with Rai staging.These results suggest that plasma IGF-1 levels in patients with CLL are correlated with tumor burden and Rai staging and therefore might be a valuable prognostic factor. Further comprehensive studies are required to support our results.Ethics Committee Approval: Study assessment and methods were approved by the local institutional ethics committee (21.01.2009/11); Informed Consent: Patient demographics and laboratory data were obtained from patient records upon obtaining oral informed consent from the patients and their information was recorded.


We performed lipid analyses at the early period of therapy in patients with chronic hepatitis C who underwent interferon (IFN)-free direct-acting antiviral (DAA) treatment, and we attempted to identify the factors that contributed to a rapid increase in the patients’ serum low-density lipoprotein cholesterol (LDL-C) concentration.We retrospectively analyzed the cases of 100 consecutive patients with HCV infection treated at the National Hospital Organization Nagasaki Medical Center: 24 patients underwent daclatasvir (DCV) and asunaprevir (ASV) combination therapy (DCV/ASV) for 24 weeks, and the other 76 patients underwent ledipasvir and sofosbuvir combination therapy (LDV/SOF) for 12 weeks. ΔLDL-C was defined as the changed in LDL-C level at 28 days from the start of therapy. To determine whether ΔLDL-C was associated with several kinds of factors including viral kinetics, we performed a stepwise multiple linear regression analysis.−10) compared to those in the DCV/ASV-treated patients . The median levels of ΔLDL-C in the LDV/SOF and DCV/ASV groups were 33.2 and 13.1, respectively. LDV/SOF combination therapy as an IFN-free regimen (p<0.001) and ΔHCV core antigen (0–1 day drop) (p<0.044) were identified as independent factors that were closely related to the ΔLDL-C.The LDL-C levels in patients treated with LDV/SOF were markedly and significantly elevated (87.45 to 122.5 mg/dl; p<10A rapid increase in the serum LDL-C concentration during the IFN-free treatment of hepatitis C was associated with the type of HCV therapy and a decline of HCV core protein. Hepatitis C virus (HCV) is a major causative agent of chronic liver diseases including steatosis, cirrhosis, and hepatocellular carcinoma. It has been estimated that worldwide there are 170 million patients with chronic liver disease, of whom most are infected with HCV . The patet al. reported that the interaction of envelope glycoprotein 1 with apolipoprotein may promote an entry into hepatocytes [Recent research into the HCV life-cycle has demonstrated a strong interaction between the virus and intracellular lipids, suggesting that host lipids play an important role in viral replication. Host serum lipids play a role in hepatitis C virion circulation and hepatocyte entry. A proportion of circulating hepatitis C viral particles are complexed with host triacylglycerol-rich lipoproteins, known as lipo-viroparticles –11. Lipoatocytes . Once heatocytes .In Japan, the treatment of chronic HCV infection is now possible with interferon (IFN)-free regimens composed of direct-acting antiviral (DAA) agents that directly inhibit viral proteins –19. MarkIFN-free DAA treatment for HCV in elderly Japanese HCV-infected patients was reported to achieve a sustained virologic response (SVR), but it was not known how these patients’ lipid profiles were altered after the SVR was achieved ,19. We fIn the present study, we performed lipid analyses in patients with chronic hepatitis C who underwent IFN-free DAA treatment at the early period of therapy, and we attempted to identify the factors that contribute to a rapid increase in the serum LDL-C concentration.Between January 2013 and December 2015, a total of 100 consecutive HCV genotype 1 patients who underwent an IFN-free regimen at the National Hospital Organization Nagasaki Medical Center were enrolled in our retrospective study. The diagnosis of chronic infection of HCV was based on the finding of continuous positivity for anti-HCV and HCV-RNA by polymerase chain reaction (PCR). The clinical condition of each patient’s liver was defined using the FIB-4 index. The presence of advanced liver disease was diagnosed by cirrhotic change in the liver shape by ultrasonography, computed tomography or magnetic resonance imaging and an FIB-4 index >3.25, which predicts the existence of significant fibrosis (F3–F4) . PatientAntiviral therapy without IFN against HCV was administered to all patients. Among the 100 patients, 24 received the combination therapy of daclatasvir  and asunaprevir  for 24 weeks (DCV/ASV). The other 76 patients received the combination therapy of ledipasvir  and sofosbuvir  for 12 weeks (LDV/SOF). LDV/SOF was available as a fixed-dose combination tablet named Harvoni® .For all patients in our cohort, a blood sample was taken at three time points: the day of IFN-free treatment start (D0), 24 hr after the first administration of an antiviral agent (D1), and 4 weeks after the start of therapy (D28). We retrieved each patient’s medical history along with the results of routine tests for blood cell counts, liver biochemistry and HCV viral load at the time of treatment and thereafter, from their medical records. Complete blood counts and biochemical tests including total cholesterol (TC), triglyceride (TG), and LDL-C were performed at D0, D1, and D28 using automated procedures in the clinical laboratories of our hospital. ΔTC and ΔLDL-C were defined as the change in each parameter from D0 to D28.10IU/ml). HCV core antigen was determined by the chemiluminescent immunoassay (CLIA) method , with the measurement range 3.0–180,000 (fmol/L). We measured the viral load by determining both the HCV-RNA and HCV core antigen values at D0, D1, and D28. For 17 of the patients, there was loss of HCV core antigen data at D1, and were therefore analyzed the viral decline by both assays in only 83 patients. The definition of ΔHCV-RNA and ΔHCV core antigen were the change of each observed value from D0 to D1.We measured the HCV viral load by performing two assays simultaneously: HCV-RNA and HCV core antigen assays. We determined the HCV-RNA by conducting a TaqMan PCR using a commercial kit , with the measurement range 1.2–7.8 .Categorical data  were compared by chi-square test. The continuous variables  were dichotomized with respect to the median value or clinically meaningful values in a multivariate analysis. P-values <0.05 were considered significant. All statistical analyses were performed using the Statistical Package for the Social Sciences software program, ver. 23.0 .10IU/ml) and that of HCV core antigen was 3.6 . The median pretreatment value of TC was 155.5 ; that of LDL-C was 84.5 , and that of TG was 96.0 .The characteristics of the patients at baseline are summarized in −9) compared to that of the patients who received DCV/ASV  (The dynamic changes in the patients’ TC and LDL-C levels during their IFN-free therapy are illustrated in = 0.054) .−10) compared to those in the DCV/ASV-treated patients   and 13.5 , respectively . Similar 0.0056) . The med 0.0056) .Patients were divided into two groups stratified by their ΔLDL-C levels. The high ΔLDL-C group was defined as having a ΔLDL-C level ≥25.0 mg/dl, using the median value of all subjects. The low ΔLDL-C group was thus defined as having a ΔLDL-C level <25.0 mg/dl. We performed a multiple logistic regression analysis to identify the factors associated with LDL-C elevation at 4 weeks from the start of therapy . Our uniWe performed a step-wise multiple linear regression analysis to determine whether ΔLDL-C was associated with age, gender, IFN-free regimen, clinical condition of liver disease, HCV-RNA, HCV core antigen level, BMI, AST, ALT, γ-GTP, platelet count, AFP, TC, LDL-C, TG, ΔHCV core antigen or ΔHCV-RNA. The results are shown in In this study, we observed a rapid increase in serum LDL-C and TC in HCV-infected Japanese patients between baseline and treatment day 28 under the treatment of their HCV infection with two types of IFN-free DAA therapy: DCV/ASV combination therapy and LDV/SOF combination therapy. Our results showed that the increases in serum LDL-C and TC during HCV therapy were strongly dependent on the type of HCV therapy, and that there was an apparent difference between these two therapies. This is the first report to demonstrate that DCV/ASV combination therapy resulted in weaker increases in serum LDL-C and TC compared to LDV/SOF combination therapy.Although we cannot yet make a conclusion as to why there was an apparent difference in the kinetics of serum LDL-C and TC provided by the DCV/ASV and LDV/SOF therapies, we are considering the following two reasons. (1) There may be a difference in the mechanism of anti-viral action between the two therapies. DCV and LDV are both NS5A inhibitors ,18, and Although DCV/ASV combination therapy is used widely in Japan, we have been unable to find studies that reported rapid increases in LDL-C and TC during at the early period of therapy. However, we found two reports that serum LDL-C and TC increased during SOF treatment. In 2015, Meissener et al. were the first to report a rapid and sustained increase in the LDL-C concentration in HCV, GT1-monoinfected patients treated for 24 weeks with SOF/RBV [Townsend et al. later reported a rapid increase in both LDL-C and TC values that was sustained during and after treatment in HIV/HCV-co-infected patients treated with LDV/SOF combination therapy for 12 weeks . They foIn the present study, we examined two types of HCV-RNA replication markers (HCV-RNA and HCV core antigen) to investigate the connection between intrahepatic HCV suppression and the increase in LDL-C during IFN-free treatment at the early period of therapy. Our comparison of the disappearance rate of serum HCV-RNA of the start of therapy to that at therapy day 28 between DCV/ASV combination therapy and LDV/SOF combination therapy revealed no significant difference of the disappearance rate of serum HCV-RNA between the two groups (LDV/SOF 87.5% vs. DCV/ASV 80.5%). There was also no significant difference in the decline of the serum level of HCV-RNA over days 0–1 between the LDV/SOF-treated patients (−2.6 log IU/ml/day) and the DCV/ASV-treated patients (−2.7 log IU/ml/day). However, interestingly, our analysis revealed that the decline level of HCV core antigen during days 0–1 was associated with a rapid increase in serum LDL-C. Another interesting result was that the day 0–1 drop in the HCV core antigen level in the LDV/SOF group (−1.8 log fmol/L/day) was significantly greater than that in the DCV/ASV group .The lipid biogenesis of the host is closely related to the RNA replication and assembly of hepatitis C virus particles. HCV core protein localizes to the surface of lipid droplets and this localization plays a critical role in HCV particle assembly and it contributes to the accumulation and production of host lipid components ,24. HerkThe important role of lipid droplets might be associated with the production of “infectious” HCV . It's coOur findings—obtained from a substantial number of patients with meaningful increases in serum LDL-C and TC induced by IFN-free treatment—have important implications for hyperlipidemia management . Imano eAs HCV continues to be eradicated with increasing frequency among HCV-infected subjects, the post-treatment viral clearance is associated with increased LDL-C and cholesterol, often to levels associated with an increased risk of CHD ,26,32. WOur study has at least two limitations. The limited number of cases (n = 100) is the first. The other is that we did not conduct an analysis that covered a longer term, and it is of interest to know whether high LDL-C levels continue after the completion of the treatments used in this population.In conclusion, our study revealed that a rapid increase in the serum LDL concentration during the IFN-free treatment of hepatitis C patients was associated with the type of HCV therapy and a decline in the level of HCV core protein. In HCV-infected patients undergoing IFN-free treatment for HCV, lipids should be monitored post-treatment for a cardiovascular risk assessment and to determine the presence of indications for lipid-modulating therapy.S1 TableAll patients data sets were included in the following file; “LDL-C analysis, DCVASV and LDVSOF n = 100. xlsx”.(XLSX)Click here for additional data file.


Polycystic ovary syndrome (PCOS) is a heterogeneous disorder, and the underlying molecular mechanisms are not clear. To date, few studies have been conducted on the altered expression of serum microRNAs (miRNAs) in women with PCOS. The present study was performed to examine the role of the serum miRNA as a biomarker for the diagnosis of PCOS and its relationship with metabolic and reproductive traits.A cross-sectional comparison was made in 21 women with PCOS and age- and body mass index (BMI)- matched 21 healthy women in an academic center laboratory between December 2008 and October 2010. We selected miRNAs that were more than 1.5-fold up-regulated or less than 0.67-fold down-regulated in women with PCOS compared with controls using the SurePrint G3 Human miRNA Microarray. Subsequently, we validated the relative expression of the miRNAs using TaqMan quantitative real-time polymerase chain reaction (RT-qPCR) assays.P-value<0.05) by RT-qPCR. The miRNA-6767-5p was negatively associated with fasting glucose (β = -0.370) and positively associated with the number of menses per year (β = 0.383) after adjustment for age and BMI (Ps<0.05). Genes targeted by miRNA-6767-5p were involved in the cell cycle and the immune system.Serum miRNA-4522, miRNA-324-3p, and miRNA-6767-5p were down-regulated in women with PCOS compared with controls in the microarray analysis. Among these miRNAs, serum miRNA-6767-5p was validated (fold change in women with PCOS/controls = 0.39, Serum miRNA-6767-5p may be a novel candidate as a molecular biomarker in the diagnosis of PCOS and may participate in the development of the metabolic and reproductive traits of PCOS. Polycystic ovary syndrome (PCOS) is a common female endocrinopathy and affects 5–10% of reproductive-age women . PCOS ismicroRNAs (miRNAs) are small, noncoding single-stranded RNA sequences of 21–25 nucleotides in length and negatively regulate gene expression at the post-transcriptional level by influencing the evolution and stability of messenger RNAs (mRNAs) . These sSeveral studies on the altered expression of miRNAs have been reported in women with PCOS. The expression of whole blood miRNA-21, miRNA-27b, miRNA-103, and miRNA-155 was significantly increased in obese women with PCOS compared with lean women with PCOS. Serum free testosterone levels were positively associated with the expression of whole blood miRNA-21, miRNA-103, and miRNA-155 . Althougth percentile  based on the testosterone levels in healthy women with regular menses as described in detail previously   .Ultrasound examinations were performed with a 7-MHz transvaginal  transducer . Ovarian volume was calculated according to a simplified formula for an ellipsoid (0.5 x length x width x thickness) . OvarianC. elegans miRNAs were spiked into serum and used as an endogenous control probe [Total RNA was extracted from 200 μl of serum samples using the Exosomal miRNA Purification kit . Synthetic ol probe . The conIn the discovery state, by comparing the relative expression of serum miRNAs, up- or down-regulated miRNAs were selected. We identified the expression of 2,550 miRNAs using the SurePrint G3 Human miRNA Microarray, Release 21.0 . Total RNA (100 ng) was treated with phosphatase and incubated at 37°C for 30 minutes. Dimethyl sulfoxide was added to the dephosphorylated RNA, followed by the application of heat and ice. Then, the dephosphorylated RNA was incubated at 16°C for 2 hours for assembly of the labeling reaction. The labeled RNA was desalted using a spin column. The desalted labeled RNA was dried with a vacuum concentrator at 45°C to 55°C for approximately 2 to 3 hours, followed by hybridization to the Agilent miRNA expression microarray at 55°C for 20 hours. Arrays were scanned using the Agilent Technologies G2600D SG12494263 microarray platform.™ II RT-PCR System  according to the manufacturer’s recommendations for oligo (dT) 20 primed cDNA synthesis. The cDNA synthesis was performed on 500 ng of RNA at 42°C. Finally, cDNA, diluted 1:2, was used in the TaqMan quantitative real-time polymerase chain reaction (RT-qPCR). RT-qPCR was performed in a ABI PRISM 7900HT Sequence Detection System  in 384-well microtiter plates using a final volume of 10 μl. Optimal reaction conditions were obtained with 5 μl of Universal Master Mix  containing dNUTPs, MgCl2, reaction buffer, Ampli Taq Gold, 90 nM primer(s), and 250 nM fluorescence-labeled TaqMan probes. Finally, 2 μl of template cDNA was added to the reaction mixture. The primer/TaqMan probe combinations were designed according to each target sequence. Amplifications were performed starting with a 10-minute template denaturation step at 95°C, followed by 40 cycles at 95°C for 15 seconds and 60°C for 1 minute. All samples were amplified in triplicate, and data were analyzed with Sequence Detector software (Applied Biosystems).Complementary DNA (cDNA) corresponding to miRNAs identified from the microarray analysis was produced using the Superscripthttp://mirdb.org/cgi-bin/search.cgi) and the TargetScan Human release 7.0 web site (http://www.targetscan.org/). The biological functions of the predicted targets were assessed using the Database for Annotation, Visualization and Integrated Discovery Bioinformatics Resources 6.7 (https://david.ncifcrf.gov/).The target genes of miRNAs were predicted using the MiRDB web site (-ΔΔCt = (ΔCt of women with PCOS - ΔCt of control subjects). The statistical analyses were performed using the SPSS 22.0 software package for Windows . The Kolmogorov-Smirnov test was used to analyze the continuous variables for normality. The quantitative variables are reported as the means ± standard deviations. The women with PCOS and control subjects with different clinical and biochemical characteristics were compared using Student’s unpaired t-test. Spearman’s rho correlation coefficient was applied to assess the correlation between differentially expressed miRNAs and the metabolic and reproductive traits of PCOS. Multiple linear regression analyses were performed to determine the independent association between the relative expression of miRNA-6767-5p and SHBG, fasting glucose, and the number of menses per year after controlling for age and BMI. A P value < 0.05 was considered significant.Array data export processing and analysis were performed using Agilent Feature Extraction v11.0.1.1. The quality of miRNAs was checked and filtered by Flag. The signal intensity of miRNAs was logarithmically transformed and quantile-normalized to achieve a normal distribution. We calculated fold changes of the normalized signal of miRNAs between the women with PCOS and control subjects. For example, a fold change in the PCOS/control ratio of 2 indicates that the expression of miRNAs in women with PCOS is 2-fold up-regulated with respect to that in control subjects. We used the comparative threshold cycle (Ct) method for relative quantification of miRNAs . The ΔCtP < 0.05).The clinical and biochemical characteristics of the women with PCOS and control subjects are summarized in P < 0.05) . Among these miRNAs, miRNA-6767-5p was validated to be differentially expressed between the two groups by RT-qPCR  , Fig 1.P < 0.05) (P < 0.05)  . In the  < 0.05) .The list of predicted target genes included 20 genes for miRNA-6767-5p. Genes targeted by miRNA-6767-5p participated in the cell cycle and the immune system process .In this study, serum miRNA-6767-5p was differentially expressed in the women with PCOS compared with the control subjects. The relative expression of serum miRNA-6767-5p was negatively associated with fasting glucose and positively associated with the number of menses per year. The genes targeted by miRNA-6767-5p were mainly involved in the cell cycle and immune system processes.lin-4 in 1993 [Since the discovery of  in 1993 , hundred in 1993 . As miRN in 1993 . Therefo2) than that of women with PCOS in the present study (22.2 ± 2.7 kg/m2). In addition, the differences in the ethnicity of individuals with and diagnostic criteria for PCOS, the sources of miRNAs, and the manual procedures and microarrays used for identification of the expression of miRNAs could have caused the different results.miRNAs can be good biomarkers of many disorders, including PCOS because the expression of miRNAs is organ-specific and related to the regulation of organ function . RecentlP < 0.05), and the expression of miR-6767-5p was negatively correlated with FAI at a marginally significant level (P = 0.057). As hyperandrogenemia is a key characteristic of PCOS, miRNA 6767-5p may be important in the pathogenesis of PCOS. In addition, abnormal expression of miRNAs has been reported to be associated with insulin resistance and diabetes [Numerous miRNAs regulate ovarian functions, such as ovarian follicular growth, atresia, and ovulation. miRNAs also regulate ovarian steroidogenesis by targeting hormone receptors and affecting hormone biogenesis and secretion . Using hdiabetes . miR-93 diabetes . The expdiabetes , it is pIn previous studies, the genes targeted by miRNAs differentially expressed in women with PCOS were involved in various biological processes. Genes targeted by serum miRNA-222, miRNA-146a, and miRNA-30c were involved in metastasis, the cell cycle, apoptosis, and the endocrine system . The tarThis is the first study to examine the aberrant expression of serum miRNAs in Korean women with PCOS. One strength of our study is that we identified a novel miRNA that had not been previously detected in women with PCOS. The other strength of our study is that we used the SurePrint G3 Human miRNA Microarray, which can identify the expression of 2,550 miRNAs, compared with only specific candidate miRNAs in the previous study, in women with PCOS . The oth


Addition of steel fibers to high strength concrete (HSC) improves its post-peak behavior and energy absorbing capability, which can be described well in term of toughness. This paper attempts to obtain both analytically and experimentally the efficiency of steel fibers in HSC columns with hybrid confinement of transverse reinforcement and steel fibers. Toughness ratio (TR) to quantify the confinement efficiency of HSC columns with hybrid confinement is proposed through a regression analysis by involving sixty-nine TRs of HSC without steel fibers and twenty-seven TRs of HSC with hybrid of transverse reinforcement and steel fibers. The proposed TR equation was further verified by compression tests of seventeen HSC columns conducted in this study, where twelve specimens were reinforced by high strength rebars in longitudinal and transverse directions. The results show that the efficiency of steel fibers in concrete depends on transverse reinforcement spacing, where the steel fibers are more effective if the spacing transverse reinforcement becomes larger in the range of 0.25–1 effective depth of the section column. Furthermore, the axial load–strain curves were developed by employing finite element software (OpenSees) for simulating the response of the structural system. Comparisons between numerical and experimental axial load–strain curves were carried out. The small-scale and large-scale column sizes were 200 mm × 200 mm × 900 mm and 400 mm × 400 mm × 1200 mm, respectively. Three different fiber volume fractions  were used, and the volumetric ratios of transverse reinforcement in the concrete core varied between 0% and 7.92%. The details of the test specimens and bar configurations are shown in c, was 100 MPa for small scale columns and 70 MPa for large-scale columns and the concrete mix proportions are summarized in et al. [et al. [The design concrete strength, f’ [et al. . In TablFor N-series small scale columns, the bars with yield strengths of 280 MPa were selected for longitudinal reinforcement, and the bars with yield strengths of 420 MPa were selected for transverse reinforcement. The bars with yield strength of 785 MPa were used as longitudinal and transverse reinforcement in H-series small-scale columns. The bar size #4 with diameter of 12.7 mm was used in all series of small-scale columns. For large-scale columns, the bar size #8 with diameter of 25.4 mm and yield strength of 685 MPa was selected for longitudinal reinforcement, and the bar size #4 (diameter of 12.7 mm) with yield strength of 785 MPa was selected for transverse reinforcement. There was a concrete block at both the top end and the bottom end of each large-scale column; therefore, the total height of each large-scale column was 2000 mm. For each small-scale column, a steel plate with thickness of 6 mm was provided at the top end and bottom end of the column to obtain the uniform stress on the column section. At least three reinforcing bars from each specified yield strength were taken to undergo the tensile test so that the average actual yield strength could be obtained. o) of all column specimens was in accordance with the ACI ITG-4.3 R-07 [gA = total cross-sectional area of column; stA = total cross-sectional area of longitudinal reinforcement; yf = yield strength of longitudinal bar; The nominal axial strength of the column at zero eccentricity  provided on both sides of each concrete recorded the concrete axial strains, as shown in All columns were subjected to concentric axial compression load. The compression tests on all series of small scale columns were conducted under a servo-hydraulic closed-hoop testing machine with a capacity of 5000 kN, where the displacement rate of the machine was 0.0375 mm/s. For the measurement of the steel strain, two electrical strain gauges were laid on one longitudinal steel bar, and there were three strain gauges on one transverse bar (close hoop). These strain gauges were located around the mid-height of column. Four LVDTs with a gauge length of 400 mm recorded the axial strains, and were located at side of and at the mid-height of each column specimen. The large-scale columns were tested in the National Center Research for Earthquake Engineering (NCREE) Taiwan, by using the Multi-axial Resting System (MATS). At least, three electrical strain gauges on one longitudinal steel bar and transverse bars (one on the close hoop bar and two on ties) were available to measure the steel strains in the concrete, and were located around the mid-height of the column. MATS can apply a maximum axial load of 60 MN, and the loading rate employed was 0.05 mm/s. co, nominal axial strength at zero eccentricity, Po, maximum axial load carried by the concrete column, Pc,max,exp, strain corresponding to the peak load, εc,max,exp, ratio of Pc,max,exp to Po, and toughness ratio, TRexp, obtained from the column specimens.The concrete axial strain was the average strain obtained from the two LVDTs provided at each of the column specimens. pullout. The equivalent bond strength, τeq, can be defined from Equation (13) [fd = diameter of the fibers; embeddedL = length of the fiber embedded into the matrix. The analytical model for computing the pullout energy proposed by Xu et al. [el al. [eq produced from fiber pullout tests and from the analytical model were 10.49 MPa and 11.52 MPa, respectively. The comparison between Xu’s model [et al. [τeq of steel fiber in concrete without providing a fiber pullout test.ion 13) .(13)τequ et al.  is emplo [el al. . By usin’s model  and the 3 .(13)τ [et al.  is validcP,max,exp was obtained from specimen H60-0.75. c,max,exp which varied between 0.0015 and 0.008. The peak strain c,εmax,exp obtained from specimen N40-0.75 was the highest peak strain if compared with peak strains obtained from other small-scale columns specimens, while the cε,max,exp of specimen H60-0.75 was the second highest. As seen in For specimens N40-0.0, N40-0.75, N60-1.0, the axial load dropped to around 77%, 78%, and 63% of the peak load, respectively, due to the buckling of longitudinal reinforcement, and the post peak loads of these three specimens approaching constant value. The pre-peak behavior (ascending branch) of specimen N90-1.5, H60-0.0, H60-0.75, H90-1.0, and H120-1.5 was similar to that of specimen N40-0.0, N40-0.75, and N60-1.0; however, the post peak load decreased slowly, and did not approach constant value, as shown in specimens N40-0.0, N40-0.75, and N60-1.0. In general, the descending branch existed due to the ductile behavior of the concrete core. The specimens H90-1.0 and H120-1.5 were low-confined specimens, because the concrete cover of these two specimens did not show more cracks at the failure stage. Furthermore, the post-peak loads on the descending branch up to a strain of 0.03 of specimens H90-1.0 and H120-1.5 were much lower than those of specimens N40-0.0, N40-0.75, H60-0.0, and H60-0.75. The specimens N60-1.0 and N90-1.5 can be categorized as moderate-confined specimens. As seen in TRexp, are summarized in The experimental toughness ratios, Furthermore, it could be observed that the use of high strength steel in the HSC column under a concentric axial compression load improved the maximum axial load carried by the column. As shown in c,max,exp varied between 0.0025 and 0.0061. The peak strain c,max,expε obtained from specimen S80-0.0 was the highest peak strain if compared with other peak strains obtained from other large-scale columns tests, while the c,max,expε of specimen S80-075 was the second highest. As presented in For specimens S80-0.0, S80-0.75 and S120-1.0, the post peak loads decreased slowly. For specimens S120-0.0, S170-0.0, and S340-0.0, the axial load tended to be straight from a load of 0 up to the first peak load, dropped to around 90% of the maximum axial load and then reached the second peak load directly. As presented in The specimen S80-0.0 was a well-confined specimen, because the concrete core failed after most of the concrete cover spalled off, as shown in The relationship of According to the test results, the presence of steel fibers is more effective when the transverse reinforcement spacing becomes larger. However, as shown by et al. [TR estimation for HSC column hybrids of transverse reinforcement and steel fibers, and can also be applied for HSC without steel fibers if IfT is equal to zero for HSC without steel fibers (Equation (11) will be exactly the same as Equation (2)).The compression test results of HSC column hybrids of transverse reinforcement and steel fibers from Lima and Giongo , Paultreet al. , and theIt,equivalent since no software provides a stress-strain model for concrete confined by transverse reinforcement and steel fibers. The stress-strain model for confined concrete was based on the model proposed by Paultre et al. [Developing axial load–axial strain curves from the numerical model was conducted in order to verify whether the use of TR in term of transverse reinforcement and steel fiber parameters is feasible to obtain confinement efficiency. OpenSees (Open System for Earthquake Engineering Simulation) , a finite et al. . Figure The cross comparison study of experimental load-strain curves and load-strain curves developed by using the numerical model is illustrated in Adding steel fibers to HSC columns can improve their post peak behavior. This is because steel fibers provide a bridge effect due to the presence of the bond strength between the steel fibers and the matrix .eqτ should be considered in accounting for fiber characteristics. The test results obtained from fiber pullout tests were verified using a progressive micromechanical model proposed by Xu et al. The average pullout energy in this experimental study was only 9.8% difference from that calculated by using the micromechanical model proposed by Xu et al. in 2011, where the equivalent bond strength eqτ can be computed by using the equation proposed by Kim et al. in 2007. Accordingly, eqτ can be calculated based on the progressive micromechanical model if no fiber pullout test results are provided.In this study, not only the fiber volume fraction and aspect ratios, but also the equivalent bond strength TR is not only influenced by the grade of rebars but also by the transverse reinforcement spacing and the fiber volume fractions.From the test results of small-scale columns, it can be observed that a TR of a specimen with high strength steel rebars may be similar to that of a specimen with normal strength rebars. This is because TR equation for HSC confined by transverse reinforcement is proposed based on the regression analysis on 69 TRs for HSC columns confined by transverse reinforcement.A TR for HSC confined by transverse reinforcement  was modified by involving fiber factor TRHSC,prediction equation to obtain the effective confinement index equivalent The The efficiency of steel fibers in concrete depends on the spacing of the transverse reinforcement, in which the steel fibers are more effective if the spacing transverse reinforcement becomes larger. The relationship of fiber factor The column specimen with no transverse reinforcement is still brittle even though the fiber volume fraction is 1.5%, as observed on specimen N00-1.5.The errors between Employing toughness ratio TR in term of transverse reinforcement and steel fiber parameters is feasible to compute the confinement efficiency of HSC column hybrids of transverse reinforcement and steel fibers. This is because TR expresses the energy absorbing capability, in which the energy absorbing capability is associated with post-peak behavior.et al. in 2010 was adopted to develop the stress-strain model for concrete confined in OpenSees. The results obtained from OpenSees well-match the results obtained from compression tests.Based on the transverse reinforcement equivalent, the confinement model proposed by Paultre TR in this study should be further verified through HSC column hybrids of transverse reinforcement and steel fibers subjected to high axial compression load and reversed cyclic loading.The proposed This paper presents an analytical study of and a series of compressive tests on HSC column hybrid confinement of transverse reinforcement and steel fibers under axial compression load. Based on the results of this study, the following conclusions can be drawn:


M. tuberculosis develop TB disease while others remain latently infected (LTBI) during their lifetime. Thus, the absence of tests able to distinguish between latent infection and active tuberculosis is one of the major limits of currently available diagnostic tools.Tuberculosis (TB) still is a major worldwide health problem, with 10.4 million new cases in 2016. Only 5–15% of people infected with M. tuberculosis antigens ESAT-6, CFP-10 and Ala-DH. Statistical analysis was performed to define the sensitivity and the specificity of the LIOSpot® TB kit for each antigen used and to set the best cut off value that enables discrimination between subjects with active TB or latent TB infection.A total of 215 patients were included in the study as active TB cases (n = 73), LTBI subjects (n = 88) and healthy persons (n = 54). Peripheral blood mononuclear cells (PBMCs) were isolated from each patient and the LIOSpot® TB anti-human IL-2 ELISpot assay was performed to test their proliferative response to Comparing the LIOSpot® TB results for each tested antigen between uninfected and infected subjects and between people with latent infection and active TB disease, the differences were significant for each antigen (p< 0.0001) but the ROC analysis demonstrated a high accuracy for the Ala-DH test only, with a cut off value of 12.5 SFC per million PBMCs and the Ala-DH ROC curve conferred a 96% sensitivity and 100% specificity to the test. For the ESAT-6 antigen, with a best cut off value of 71.25 SFC per million PBMCs, a sensitivity of 86% and specificity of 36% was obtained. Finally, the best cut off value for CFP-10 was 231.25 SFC per million PBMCs, with a sensitivity of 80% and a specificity of 54%. Thus, as for IGRA assays such as Quantiferon and T-Spot TB tests, ESAT-6 and CFP-10 are unable to distinguish LTBI from active TB when IL-2 is measured. On the contrary, the IL-2 production induced by Ala-DH, measured by LIOSpot® TB kit, shows high sensitivity and specificity for active TB disease.This study demonstrates that the LIOSpot® TB test is a highly useful diagnostic tool to discriminate between latent TB infection and active tuberculosis in adults patients. Tuberculosis (TB) still is a major health problem worldwide. In 2016, there were 10.4 million new estimated cases worldwide. TB ranks above HIV/AIDS as one of the leading causes of death from an infectious disease; 1.7 million deaths were estimated in 2016 [Mycobacterium tuberculosis (MTB) is initially contained by the host immune system, resulting in a latent TB infection (LTBI) which is characterized by the presence of immune responses to MTB in the absence of clinical, radiological and microbiological evidences [Incident TB cases reflect a little percentage of the global infection burden: indeed in the great majority of immunocompetent persons, infection with vidences ; 5–15% ovidences , 4.The tuberculin skin test (TST) and MTB specific interferon-gamma (IFN-γ) release assays (IGRAs), such as QuantiFERON-TB Gold In-Tube (QFT-G-IT) assay  and T-SPOT TB assay  are still the main tools used for the diagnosis of TB infection. Although the newer IGRAs show some improvements over TST , 6, 7, nin vitro diagnostic method for differential diagnosis between active TB and LTBI.Since the reactivation of TB can be averted by preventive treatment, it is important to identify new biomarkers for the diagnosis of LTBI by setting a new M. tuberculosis to anaerobic dormant stage in LTBI. Ala-DH of M. tuberculosis is a unique enzyme involved in peptidoglycan biosynthesis since it only accepts L-alanine as substrate in contrast to Ala-DH from all other organisms studied, which also use serine as a substrate [M. bovis and in M. bovis BCG making it highly specific to M. tuberculosis as the cause of world-wide pulmonary and extra-pulmonary tuberculosis. Even though this protein is present in other pathogenic mycobacteria such as M. marinum and M. ulcerans, there is no overlap between symptoms of TB and the low prevalence diseases caused by M. marinum or M. ulcerans. The same shall hold good for all other type of microorganisms possessing Ala-DH enzyme. It is worth mentioning that ESAT6 and CFP10 have been used world-wide in the Quantiferon and T-Spot kits and are regarded as specific to M. tuberculosis, but these antigens are also present in M. marinum and M. ulcerans. To the best of our knowledge, there are no reports of significant, false diagnosis by the use of the above mentioned IGRA tests showing that infection and diseases caused by non-tuberculous mycobacteria (NTM) do not pose serious problems as far as cytokine based TB diagnosis is concerned. An extensive, prospective cohort study of 210 BCG- vaccinated neonates in Southern India was published by Dhanasekaran et al. [Our group has studied this Ala-DH for more than 25 years and have published, for the first time, its biochemical ,10, moleubstrate ,10. Thisn et al.  on effecn et al. .M. tuberculosis bacilli [Given that Ala-DH is not specific for  bacilli , 15 we c bacilli .Although other authors also use IL-2 to detect response in MTB infected subjects , 18, 19 This study is focused on improving TB diagnosis by developing a new blood test to discriminate between active and latent TB infection according to the best cost/effectiveness ratio. A new ELISpot test, called LIOSpot® TB, with high specificity and sensitivity was developed based on our previous home-made ELISpot.  The new This study was performed on peripheral blood samples collected from patients with confirmed active TB, from subjects diagnosed with LTBI and from healthy donors, who were consecutively enrolled during the period between September 2014 and August 2016 by the Careggi University Hospital, Florence, Italy. Subjects below 18, pregnant women, HIV/AIDS and all those people with any known immunocompromising condition  were excluded from the study. None of the healthy patients had recent exposure to active pulmonary TB cases.Following the approval by the “Area Vasta Centro, Regione Toscana, Ethical Committee” (BIO 14.013), each patient, previously informed of the aim of the study, signed an informed consent.All subjects were tested with TST  according to the Mantoux method  and withThe subjects enrolled in the study were classified as active TB patients, LTBI patients or healthy people in accordance with the current guidelines , 22, 23.Mycobacterium strain and to detect rifampicine resistance; then mycobacterial-specific solid and liquid culture media were used for the isolation of the infectious agent.Active TB patients, in addition to TST positivity and a generally positive QFT-G-IT that can be negative in a certain number of cases, were detected through clinical, microbiological and radiological findings. The diagnosis' confirmation required MTB identification through fluorescence microscopy, polymerase chain reaction (PCR) or cultural assays upon biological samples, such as sputum or bronchoalveolar lavage (BAL) for respiratory diseases, tissue biopsies, drainage liquid or needle aspirates for extrapulmonary localizations. In particular, all samples were tested with auramin-rhodamine fluorescence microscopic examination to detect acid-alcohol resistant bacilli (BAAR); PCR-based methods like Artus MTB PCR Kit  and GeneXpert MTB/RIF assay  were used to identify the LTBI was diagnosed by a positive test for MTB infection in persons without history of BCG vaccination or by both TST and IGRA positivity in BCG vaccinated subjects, provided the exclusion of active TB by medical history, clinical, radiologic, and microbiologic evaluations , 24, 25.A subject without BCG vaccination and discordant results between TST and QFT-G-IT was attributed to his study group based on the TST result according to the Mantoux method that is still the standard test for LTBI diagnosis; however IGRAs are recommended to confirm LTBI diagnosis in TST positive BCG vaccinated individuals , 5, 26. Consequently to the diagnosis, the 215 patients were classified as active TB cases (n = 73), LTBI cases (n = 88) and healthy individuals (n = 54). Lymphoprep was purchased from Fresenius Kabi Morge AS , RPMI 1640 medium from Biochrome , L-glutamine from Euroclone , beta-mercaptoethanol from Sigma-Aldrich  Na-pyruvate and Non-essential aminoacids from Thermofisher scientific , penicillin/streptomycin from PAN biotech , fetal bovine serum .M. tuberculosis ESAT-6, CFP-10 and Ala-DH recombinant antigens, all produced at LIONEX to a purity of more than 98% and endotoxin free; Phytohaemagglutinin mitogen (PHA) was used as a positive control.LIOSpot® TB  is an anti-human IL-2 Elispot kit containing 6 PBMCs/ml.Peripheral blood mononuclear cells (PBMCs) were isolated from blood samples of each enrolled patient, within 8 hours after venipuncture to ensure cell activity, by Ficoll-Hypaque density gradient centrifugation. Briefly, blood was layered on top of Lymphoprep, density gradient of 1.077 g/mL, and centrifuged at 800 x g for 25 minutes at room temperature (15–25°C) without brake. PBMCs layer was harvested and washed two times in PBS, pH 7.4. Cells were counted using the Burker chamber and then transferred into RPMI 1640 complete medium , with 5% fetal bovine serum to obtain a concentration of 2.5x105 cells per 0.1ml/well, working under sterile conditions. The plate was incubated in a humidified incubator at 37°C, 5% CO2 for 16–24 hours: during incubation time the antigen will activate specific cells to release IL-2 that will be captured by the antibody at the bottom of the well. After the incubation time wells content was discarded and the plate was washed five times with a wash buffer (PBS—0.05% Tween-20) before adding the anti-human IL-2 biotinylated detection antibody to each well. After one hour of incubation, wash steps were repeated and the conjugated horseradish peroxidase (HRP)-streptavidin solution was then added. The plate was incubated again for one hour at room temperature (RT) and, after being washed as already described, TMB  substrate solution was dispensed into each well and incubated for ten minutes at RT in the dark, until spots were visible. Following the development of spots, the wells' content was discarded and a stop solution was used. The plate was dried and the number of SFC (spot forming cells) was counted by an automated ELISpot reader using the AID EliSpot Software Version 3.2.3.The LIOSpot® TB anti-human IL-2 ELISpot kit contains a 96-well plate coated with anti-human IL-2. First we added the positive control (PHA) and the negative control (medium) as single determinations and the three different antigens ESAT-6, CFP-10 and Ala-DH in double determination (5μg/ml); then PBMCs from each patient were seeded in order to have 2.5x10The SFC count in PHA positive control wells should be more then 50 or show saturation to confirm cellular functionality and vitality. In the negative control it would be expected to have few or no spots; the number of SFC in the negative control is subtracted from the SFC mean value of the wells containing the ESAT-6, CFP-10 and Ala-DH antigens. Results were expressed as number of SFC per million of PBMCs.Descriptive statistics were used for the calculation of absolute frequencies and percentages for qualitative data, as well as for mean, median (IQR), and standard deviation of quantitative data.All distributions of ELISpot test results for healthy patients, LTBI subjects or active TB patients were compared using the Mann-Whitney inferential test or the Student t-test.P <0.05 was considered statistically significant.Test performance in terms of sensitivity (ability of the test to identify the true positive subjects) and specificity (ability of the test to identify the true negative subjects) was evaluated for each antigen by a ROC (Receiving Operating Characteristic) curve, the elective validation method of a quantitative diagnostic test in a population. The proportion of patients correctly diagnosed, that is the test accuracy, is proportional to the area under the curve (AUC), which can assume values between 0.5 (50% accuracy) and 1 (100% accuracy). According to the classification proposed by Swets the test is not accurate for AUC = 0.5, the test is poorly accurate for 0.5 <AUC≤ 0.7, the test is moderately accurate for 0.7 <AUC≤ 0.9, the test is highly accurate for 0.9 <AUC< 1 and the test is perfect for AUC = 1 . The ROCFinally, the correlation between the ELISpot diagnostic test for each antigen and the diagnosis performed on each patient using the current methods was calculated by Cohen's Kappa coefficient according to which there would be slight concordance for the value of k = 0.2, poor concordance for the value of K in the range 0.2–0.4, moderate concordance for k value in the range 0.4–0.6, substantial concordance for k value in the range 0.6–0.8, good concordance for k value in the range 0.8–1.Statistical analysis were performed with SPSS for Windows software version 20.0.Two hundred and fifteen subjects were enrolled in this study: 73 patients with active TB, 88 with LTBI and 54 as uninfected controls. We performed LIOSpot® TB according to kit manual on PBMCs isolated from a blood sample of every participant by stimulation with MTB antigens such as ESAT-6, CFP-10 and Ala-DH; the T cell specific response to each antigen was evaluated in terms of IL-2 production. .Mann-Whitney inferential test or Student t-test were used to compare ELISpot results for IL-2, as SFCs per million PBMCs, between healthy patients, LTBI subjects and active TB ones referring to each tested antigen. Comparing the results of infected and non-infected there were significant differences for all the antigens (p≤0.0001); similarly, comparing the results of patients in LTBI and the group of active TB patients there were significant differences for all three antigens too (p≤0.0001) .ROC curve analysis was performed for each antigen in order to establish the best cut off of the ELISpot test for IL-2 in discriminating between LTBI and active TB.Considering the response to Ala-DH antigen, a best cut off value of 12.5 SFC per million PBMCs to have a 96% sensitivity and 100% specificity was established. The area under the ROC curve was 0.971 (IC 95%: 0.939–1), so the test was highly accurate in correctly diagnosing subjects with active disease and those with latent infection.For the ESAT-6 antigen, with a best cut off of 71.25 SFC per million PBMCs, a sensitivity of 86% and specificity of 36% was obtained. The area under the ROC curve was 0.713 (IC 95%: 0.632–0.794), the test is moderately accurate. Finally, the best cut off for CFP-10 was 231.25 SFC per million PBMCs, with AUC = 0.693 (IC 95%: 0.611–0.774); The test had a low accuracy, with sensitivity of 80% and specificity of 54% .Once the best threshold value for the ELISpot IL-2 result was set for each specific antigen to discriminate between subjects with active TB and subjects with LTBI, the correlation between ELISpot and TST, that is still the standard test for MTB infection diagnosis, was assessed for every antigen by computing The Cohen's Kappa coefficient and interpreting the data as described above.The concordance between the ELISpot test for IL-2 and the TST test for all the results obtained in the 215 patients with Ala-DH, ESAT-6 and CFP-10 is: 92.56% (k = 0.82 very good concordance), 77.2%  and 63.7%  respectively .The concordances between the different assays are shown in Up to now, there are no biomarkers allowing to differentiate between active TB and LTBI. During LTBI, MTB lives in a non-replicating state, enclosed into the granuloma structure, as long as the host remains immunocompetent; in this period the bacillus still maintains the ability to reactivate and produce active disease when the host immune response is impaired , 29.Studying gene expression profiles and proteomic analysis in both active and quiescent mycobacteria, a number of genes that are differently regulated during the latency phase have been found, in comparison to those expressed during active infection , 30. DifMTB Ala-DH catalyzes reversible conversion of pyruvate to alanine, and glyoxylate to glycine concurrent with the oxidation of NADH to NAD  to maintThus MTB Ala-DH was thought to be a useful tool to discriminate between LTBI and active TB  and our Cytokines play an important role in cell mediated immune responses to MTB infection. The IFN-γ production by activated T cells has been widely considered to play a crucial role in protection against MTB infection .In the last decade, IGRAs became a landmark in the diagnosis of MTB infection. However, these assays do not discriminate between latent infection and active tuberculosis disease, as TST.The choice of setting the LIOSpot® TB assay on IL-2 cytokine production is due to the fact that recent studies demonstrate the importance of this cytokine to discriminate between latent and active tuberculosis infection, thus being a new possible diagnostic biomarker , 37, 38;According to these findings, LIOSpot® TB was set as an anti human IL-2 ELISpot assay able to detect IL-2 production after PBMCs stimulation with ESAT-6, CFP-10 and Ala-DH of MTB.In our study, all the healthy subjects showed no significant production of IL-2 (SFC per million of PBMCs) in response to T cell stimulation by the three different antigens: Ala-DH (0: 0–0), ESAT-6 (2.5: 0–17.5) and CFP-10 (0: 0–15). Furthermore it is of note that none of the 21 BCG-vaccinated healthy subjects gave positive IL-2 responses to Ala-DH. The LIOSpot® TB detected a significant production of IL-2 after stimulation with ESAT-6 (392.5: 140–1300), CFP-10 (480: 325–1590) and with Ala-DH (280: 85–1120) in 73 patients group with active TB. Among people in the LTBI group, despite the smaller but substantial production of IL-2 in response to ESAT-6 and CFP-10 stimulation (180: 30–340 and 192.5: 77.5–660 SFC per million of PBMCs respectively) compared to active TB cases, Ala-DH induced a very low amount of IL-2 production by stimulated T cells (7.5: 2.5–10 SFC per million of PBMCs).Even though comparing the LIOSpot® TB results for each tested antigen between uninfected and infected subjects and between people with LTBI and active TB, all the differences were significant (p< 0.0001), the ROC analysis demonstrated a high accuracy of the test only for Ala-DH: with a cut off value of 12.5 SFC per million PBMCs, the Ala-DH ROC curve conferred 96% sensitivity and 100% specificity to the test. Thus, the LIOSpot® TB test is highly accurate and is able to make a differential diagnosis between subjects with active TB and those with LTBI.For the ESAT-6 antigen, with a best cut off value of 71.25 SFC per million PBMCs, a sensitivity of 86% and specificity of 36% was obtained. Finally, the best cut off value for CFP-10 was 231.25 SFC per million PBMCs, with sensitivity of 80% and specificity of 54%.Despite the low specificity, these thresholds confer to the LIOSpot® TB the ability to detect true tuberculosis infection.Overall, the present study demonstrates that the LIOSpot® TB test is a very useful diagnostic tool to discriminate between LTBI and active tuberculosis infection.S1 FileIL-2 based ELISpot results for each of the active TB, LTBI patients and healthy subjects.(PDF)Click here for additional data file.


A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians. Unmanned aerial vehicles (UAVs) have been widely used in many fields, such as chemical vapour detection , nature (1)Both V-J and HOG + SVM are sensitive to objects’ in-plane rotation, therefore they only can detect vehicles when the orientations of vehicles are known and horizontal. Because vehicle orientations in UAV images are usually unknown and even changing, the detection accuracy  of these two methods has been significantly lowered. As shown in (2)The efficiency  of both V-J and HOG + SVM is downgrading with the increase of the detection load  in a frame. As shown in For traffic and emergency monitoring, one of the essential but challenging tasks is vehicle detection. Many traditional methods, like background subtraction , frame d(1)To address the challenge that both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, we adopt a roadway orientation adjustment method. The basic idea of this method is to first measure the orientation of the road, and then rotates the road according to the detected orientation so the road and on-road vehicles will be horizontal after rotation so the original V-J or HOG + SVM methods can be applied to achieve fast detection and high accuracy. More importantly, different with some existing solutions for the issue of unknown road orientation e.g., ,15,18), ,18, 15,1(2)To address the issue of descending detection speeds for both V-J and HOG + SVM and achieve better efficiency, we integrate V-J and HOG + SVM methods based on their different descending trends of detection speed and propose a hybrid and adaptive switching strategy which sophistically searches for, if not the optimal, at lease improved solution, by switching V-J and HOG + SVM detection methods based on the change of detection speed of these two methods during the detection. This switching strategy, combined with the road orientation adjustment method, significantly improves the efficiency and effectiveness of vehicle detections from UAV images.This research aims to improve the effectiveness and efficiency of vehicle detection from UAV images by addressing the above mentioned two issues. Particularly, this research proposes a new hybrid vehicle detection scheme which integrates both V-J and HOG + SVM methods. The proposed scheme has two unique features which are designated to solve the above mentioned two issues:The rest of the paper is organized as follows: A large amount of research has been performed on vehicle detection from UAV images over the years. Many of them applied some traditional methods, such as background subtraction, frame difference, optical flow, etc. For example, Azevedo  applied In recent years, object detection algorithms have become popular for vehicle detection from UAV videos. Generally speaking, object detection algorithms are less sensitive to image noise, background motions and scene complexity, therefore are more robust for vehicle detections from UAV videos. For example, Viola and Jones  developeThe V-J scheme is based on multiple cascaded Haar-like classifiers . The basThe evaluation of the strong classifiers generated by the AdaBoost learning process can be done quickly, but it is not fast enough to process in real-time. For this reason, the strong classifiers are arranged in a cascade in order of complexity. In each cascade, each successive classifier is trained only on those selected samples which pass through the preceding classifiers. If at any stage in the cascade a classifier rejects the sub-window under inspection, no further processing is performed. The cascade therefore has the form of a degenerate tree. This degenerative decision-tree structure can eliminate negative regions as early as possible during detection to focus attention on promising regions of the image. Therefore, this detection strategy dramatically increases the processing speed of the detector, provides an underlying robustness to changes in scale, and maintains achievable real-time performance.Before applying the V-J method, the critical first step is to build a sample library which provides a training set to include both positive and negative images. As mentioned in ,25, the All positive images were further transformed into gray scale images and normalized to a compressed size of 40 × 20 during our tests. Each image was used to calculate a complete set of Haar-like features, and in total, 479,430 Haar-like features were extracted from all images. These 479,430 features were further trained into 469 most significant features by applying the Gentle AdaBoost algorithm . FinallyStep 1:Gradient computation. Step 1 computes the gradient values and orientations of all pixel units in the image by applying the 1-D centered point discrete derivative mask with the filter kernel  in one or both of the horizontal and vertical directions.Step 2:Orientation binning. Step 2 is to create the cell histograms. In this step, the image was divided into cells, and the 1-D histogram Step 3:Descriptor blocks. Step 3 groups cells together into larger, spatially connected blocks Step 4:Block normalization. Step 4 is to normalize blocks in order to account for changes in illumination and contrast. A cell can be involved in several block normalizations for the overlapping block, since each block consists of a group of cells. By concatenating the histograms of all blocks, the feature vector Step 5:SVM classifier. The final step is to feed the descriptors into the SVM classifier. SVM is a binary classifier which looks for an optimal hyperplane as a decision function.The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. HOG was first described by Dalal and Triggs  and achiHOG + SVM will use the same sample library  as for the V-J vehicle detector. To extract the HOG feature, each sample image needs to be first normalized to a compressed size of 40 × 20 pixels; then blocks (8 × 8) with 4-pixel stride step and 9 histogram bins (each bin is corresponding to 20° of orientation) were used to calculate the HOG feature vector. So for an image with 40 × 20 pixels, a total of 36 blocks can be identified A roadway orientation adjustment method. The proposed detection scheme adopts a roadway orientation adjustment method to address the roadway rotation issue. The idea is straightforward: first, measure the orientation of the road using the line segment detector (LSD) ; second,(2)A detector switching strategy. The proposed scheme further develops an adaptive switching strategy to integrate V-J and HOG + SVM methods based on their different descending trends of detection speed in order to improve the efficiency. Basically, this strategy “intelligently” switches the detection methods between V-J and HOG + SVM to choose the one which has faster detection speed. As shown in This research aims to address two critical issues of V-J and HOG + SVM by developing a hybrid vehicle detection scheme. In detail, the proposed scheme integrates the following two improvements:The overall framework of the proposed vehicle detection method is shown in As mentioned before, when directly applying V-J or HOG + SVM to detect vehicles from UAV images, the detection rate is significantly low. The reason, as mentioned before, is that the original V-J and HOG + SVM schemes can only detect vehicles with orientations which are aligned with the detectors . To address this issue, this research proposes a road orientation adjustment method.(1)First, original color images are extracted from aerial videos and then transformed into gray scale images a;(2)Second, the LSD algorithm  is appli(3)Third, the orientation of each detected line segment  derived c. The an(4)Last, to minimize in-plane rotation jitters, the final rotation angle he image d. The deEssentially, the proposed road orientation adjustment method is to rotate the image according to the orientation of the road, i.e., the angle between the road and the horizontal of the image. After rotation, the road and on-road vehicles will be aligned with the vehicle detectors. As shown in (1) Straight Line Segments Detection: The proposed method first applies the LSD algorithm [lgorithm  to deteclgorithm , which rlgorithm  and the (2) Road Orientation Estimation by Relative Histogram: As shown in Step 1:Identify Step 2:Define 180 class intervals: Step 3:Determine the frequency,Step 4:Calculate the relative frequency  of each class by dividing the class frequency by the total number n in the sample, i.e., Step 5:Draw a rectangle for each class with the class interval as the base and the height equal to the relative frequency of the class to form a relative histogram c;Step 6:Identify (3) Image Rotation Angle Estimation by First-Order Lag Filtering: To minimize the impact of the jitters caused by UAVs, the first-order lag filtering algorithm is further applied to calculate the weighted average of the estimated road orientations of current and previous frames. The final image rotation angle for current frame The final step is to rotate the image by A highlight of this method is that each UAV image only needs to be rotated once. A visual comparison of vehicle detections using V-J scheme without and with road orientation adjustment is presented in The proposed vehicle detection scheme further adopts a vehicle detector switching strategy to improve detection speed. This strategy is based on the speed characteristic lines see b of the From the flowchart, we can see that for any frame The UAV system used in this research is equipped with a quadcopter (model: Phantom 2) airborne platform and a Gopro Hero Black Edition 3 aerial camera see . A 3-axiThe evaluation was based on low-altitude UAV videos captured from five different scenarios with diverse traffic and weather conditions . These dDuring evaluation, in order to avoid the situation where the same vehicle has been detected multiple times in different frames, we extract detection images each 20 s from the 10-min video for comparison. Because the length of the road segment in an image is about 160 meters, most likely a vehicle will pass the road segment in 20 s. This significantly reduces the possibility of one vehicle being detected multiple times. Note when traffic is congested, it is still possible that some slow-moving vehicles will be detected more than once. All the experiments were conducted using C++ implementation on a laptop computer  with an Intel i5-4300M @ 2.60 GHz CPU and 8 GB DDR3 memory.detection speed (f/s), Correctness, Completeness, and Quality, defined in Equation (4):true positives are the number of “true” detected vehicles; false positives are the number of “false” detected objects which are non-vehicle objects; false negatives are the number of vehicles missed. In particular, Quality is considered as the strictest criterion, which contains both possible detection errors . Note that a successful “detection” is defined as a correct detection of a vehicle in one frame.The performance of vehicle detection is evaluated by the following four typical indicators: Completeness and Quality. Furthermore, by combining the vehicle detector switching strategy, the proposed method can achieve fast vehicle detection. To fairly verify these two points, the proposed method is compared with nine other methods:(1)ViBe, a universal background subtraction algorithm ;(2)Frame difference  referre;(3)Original V-J method (referred as V-J in (4)Rotate each image every 20° from 0° to 180° and detect nine times using the original V-J method (referred as V-J + 9 in (5)Original V-J method combines with the proposed road orientation adjustment method only (referred as V-J + R in (6)Original HOG + SVM (referred as SVM in (7)Rotate each image every 20° from 0° to 180° and detect nine times using the original HOG + SVM method (referred as SVM + 9 in (8)Original HOG + SVM method combines with the proposed road orientation adjustment method only (referred as SVM+R in (9)Apply the proposed vehicle detector switching strategy to integrate V-J and HOG + SVM (without road orientation adjustment) (referred as V-J + SVM + S in (10)Apply the proposed vehicle detector switching strategy and road orientation adjustment method to integrate V-J and HOG + SVM .Conceptually, by incorporating the road orientation adjustment method, the proposed vehicle detection method will be insensitive to road orientation changes and therefore can achieve high As ViBe  and framCorrectness, Completeness, and Quality.Also, as mentioned above, for each scenario, a 10-min video with the resolution of 1920 × 1080 was used for testing. The detection speed for each method was computed as an average of each 10-min video. Note vehicle detection was performed on the entire image (1920 × 1080). After detection, we only extracted images every 20 s in order to avoid that the same vehicle in different frames has been detected multiple times. So for each scenario, totally of 30 detected images were used for computing Quality (82.32%) with fast speed (1.17 f/s). Some comparisons are presented as follows:(1)Vibe & Frame Difference: These two methods achieved fast detection speed but with low Quality (54.24% & 49.03%) which are too low to be accepted for real-world applications. The reason is that some non-vehicle objects (such as tricycles and moving pedestrians) lead to many false positives. Besides, slow-moving or stopped vehicles and some black vehicles which have similar colors with the road surface cannot be detected during detection.(2)V-J vs. V-J + 9: The Completeness (76.91%) of V-J is low, this is because many vehicles that are not parallel to horizontal cannot be detected, thus generating many false negatives. The Completeness (92.31%) of V-J + 9 is significantly higher than the original V-J. After images were rotated every 20° from 0° to 180° and detected 9 times, vehicles of different orientations can be detected. However, repeating detections of the same image lead to more false positives and greatly increase detection time. The Correctness of V-J + 9 (76.10%) is lower than that of the original V-J (83.22%). The detection speed of V-J + 9 (0.079 f/s) is also significantly slower than V-J (1.14 f/s).(3)SVM vs. SVM + 9: The comparison of SVM and SVM + 9 also demonstrates similar results as V-J vs. V-J + 9. SVM + 9 achieved higher Completeness (89.21%) than SVM (73.01%) but with low Correctness and detection speed.(4)V-J vs. V-J + R: V-J + R achieves higher Completeness (92.37%) than V-J (76.91%); because by incorporating road orientation adjustment, on-road vehicles of unknown orientations will be aligned with the horizontal direction which can be detected by the original V-J detector. V-J + R achieves higher Quality (82.09%) than V-J (66.79%), but the detection speed of V-J + R is slower than the original V-J due to two reasons: (1) the road orientation adjustment step will cost some time for road orientation detection and image rotation; and (2) after image rotation and road alignment, many more vehicles in the UAV image need to be detected.(5)SVM vs. SVM + R: The method SVM + R also achieves higher Quality (81.53%) than the original SVM method (64.28%). Similar to V-J + R, the detection speed of SVM + R is slower than the original SVM.(6)V-J + R vs. V-J + 9: V-J + R achieves slightly higher Completeness (92.37%) than V-J + 9 (92.31%), because those rotated vehicles in V-J + 9 are in fact not exactly aligned with the horizontal direction, therefore may not adapt the original V-J detector well. Also, V-J + R achieves faster detection speed (0.88 f/s) than V-J + 9 (0.079 f/s). The comparisons demonstrate that the proposed road orientation adjustment method can improve both the Completeness and Quality compared with the original V-J and HOG + SVM methods, but leads to a slightly slower detection speed.(7)SVM + R vs. SVM + 9: Similarly, SVM + R achieved higher Completeness (91.13%) than SVM + 9 (89.21%) and higher detection speed.(8)V-J vs. SVM vs. V-J + SVM + S: The proposed switching method  achieves faster detection speed (1.27 f/s) than both the original V-J (1.14 f/s) and SVM (1.07 f/s) methods, because the proposed switching strategy can automatically choose the faster method between V-J and HOG + SVM during the detection. Note that, without the road orientation adjustment, the proposed switching method only achieves low Quality (65.08%), which is similar to V-J (66.79%) and SVM (64.28%).(9)V-J + SVM + R + S: Our method, which combines the road orientation adjustment method, achieves the best Quality (82.32%) than other nine methods. The detection speed of our method is slower than V-J + SVM + S, which is a trade-off between high Quality and fast detection speed. However, our method is still faster than the original V-J and SVM methods. The detection speed of 1.17 f/s is acceptable for real-time applications.The testing results of ten methods are presented in Overall, the proposed method achieves good vehicle detection performance with fast speed. Particularly, our method is insensitive to on-road vehicles’ in-plane rotation. Therefore, the proposed method can be performed on videos captured from moving UAV platforms  without the need of image registration ,31 or adThe proposed road orientation adjustment method can be applied for roadways with more than one orientation. Here we present some testing results for roads with two orientations. As shown in Besides, it should be mentioned that the proposed road orientation adjustment method can only extract road orientations of straight roads. It will be difficult to extract the orientations of curve roads or very smaller roads. This also leads to some false detections  during our vehicle detection when applying our method to detect vehicles on curve or small roads.In this paper, we also compared the adopted LSD method with other line detection algorithms ,32,33. PThe video images used for our testing have relatively high resolution of 1920 × 1080. But technically, our method can also work with images with low radiometric quality. Particularly, we performed our roadway orientation method on a low radiometric quality satellite image see . It can To be clear we did not use oriented, mosaicked images for detection in this research because mosaicked images may contain “ghost” vehicles , as markOne drawback of the proposed vehicle detection method is its incapability of detecting turning vehicles. The roadway orientation adjustment can only rotate the image according to the orientation of the road. For turning vehicles, their orientations are changing during turning process and not aligned with the V-J and HOG + SVM detectors. This creates difficulties for vehicle detection. The original V-J and HOG + SVM methods also have this problem.It is worth mentioning some recently developed state-of-the-art methods ,20,21, wSimilarly, Liu and Mattyus  developeT = {24,120,240,360,480,600,720}  have been conducted. The testing was applied on the same datasets as shown in T = 24, the corresponding detection speeds were marked with red cross-shaped “+” shown in During our testing, the switching time interval, thod see  is 24 (iIn this paper, a new hybrid vehicle detection scheme which integrates V-J and HOG + SVM methods is proposed. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image so that roads and on-road vehicles in images are aligned with the vehicle detector. After rotation, the original V-J or HOG + SVM methods can be applied to achieve higher accuracy. To address the issue of descending detection speed for both V-J and HOG + SVM, the proposed scheme further develops a hybrid and adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to achieve better detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, it is worth mentioning that the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, therefore it has great potentials of wide field applications.However, the proposed vehicle detection scheme has difficulties to address turning vehicles. The future research will aim to address this problem. Some recently developed state-of-the-art methods ,20,21,37